10 guides
Diligence & quality
How to vet a dataset, a vendor, and the rights behind them.
Guides in this topic
5 min read
How to inventory an audio or video dataset
Build a file-level inventory before estimating volume, requesting a quote, or preparing a dataset for review. Count known durations and record the technical properties buyers need.
5 min read
Recorded hours vs usable dataset hours: how to count media volume
File-hours, unique activity-hours, speech-hours and accepted hours answer different questions. Define the unit before comparing collections or recording offers.
5 min read
How to write a dataset card buyers can evaluate
Document the dataset’s purpose, origin, collection process, limitations and permissions evidence. A useful card makes unknowns visible instead of hiding them behind marketing language.
5 min read
Dataset manifest vs datasheet vs dataset card
A manifest identifies files. A datasheet records structured answers about a collection. A dataset card explains the collection to users. Most serious deliveries benefit from all three.
5 min read
WER vs CER: calculate and interpret speech recognition errors
Compare model transcripts with verified references using word error rate and character error rate. Record normalization settings so results can be reproduced and compared fairly.
5 min read
How to evaluate low-resource language transcripts
Build a small, reviewed reference set, choose explicit text conventions, and inspect errors by declared language and recording condition before expanding a speech dataset.
7 min read
What to check before licensing a dataset
A pre-purchase diligence pass for AI teams licensing audio, video, or UGC. Consent records are the hard stop.
7 min read
How to evaluate a training-data vendor
A repeatable way to judge a vendor on provenance and rights auditability, not just samples and turnaround.
7 min read
Red flags when buying training data
The warning signs that a dataset will cost you later: too clean, no consent records, vague sourcing, and more.
7 min read
Datasheets for datasets: a buyer’s template
Turn the academic datasheet idea into a practical diligence form. Blank fields become findings.
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